from typing import Dict, Any
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from scripts.rag_chat import build_general_qa_chain

def build_router_chain(model_name=None):
    general_qa = build_general_qa_chain(model_name=model_name)
    llm = ChatOpenAI(model_name=model_name or "gpt-4o-mini", temperature=0.0)

    # This prompt asks the LLM to choose which "mode" to use
    router_prompt = ChatPromptTemplate.from_template("""
You are a routing assistant for a chatbot. 
Classify the following user request into one of these categories:
- "code" for programming or debugging
- "summarize" for summary requests
- "calculate" for math or numeric calculations
- "general" for general Q&A using course files

Return ONLY the category word.

User request: {input}
""")

    router_chain = router_prompt | llm | StrOutputParser()

    class Router:
        def invoke(self, input_dict: Dict[str, Any]):
            category = router_chain.invoke({"input": input_dict["input"]}).strip().lower()

            print(f"[ROUTER] User query routed to category: {category}")

            if category == "code":
                prompt = ChatPromptTemplate.from_template(
                    "As a coding assistant, help with this Python question.\nQuestion: {input}\nAnswer:"
                )
                chain = prompt | llm | StrOutputParser()
                return {"result": chain.invoke({"input": input_dict["input"]})}

            # elif category == "summarize":
            #     prompt = ChatPromptTemplate.from_template(
            #         "Provide a concise summary about: {input}\nSummary:"
            #     )
            #     chain = prompt | llm | StrOutputParser()
            #     return {"result": chain.invoke({"input": input_dict["input"]})}

            elif category == "summarize":
                # 1. Use RAG to retrieve relevant docs
                rag_result = general_qa({"query": input_dict["input"]})

                # 2. Extract docs and prepare text
                source_docs = rag_result.get("source_documents", [])
                combined_text = "\n\n".join([doc.page_content for doc in source_docs])

                # 3. Run the summarizer chain on the retrieved text
                from scripts.summarizer import get_summarizer
                summarizer_chain = get_summarizer()
                summary = summarizer_chain.run(combined_text)

                # 4. Add sources if any
                sources = list({str(doc.metadata.get("source", "unknown")) for doc in source_docs})
                if sources:
                    summary += f"\n\n📚 Sources: {', '.join(sources)}"

                return {"result": summary}


            elif category == "calculate":
                prompt = ChatPromptTemplate.from_template(
                    "Solve the following calculation step-by-step:\n{input}"
                )
                chain = prompt | llm | StrOutputParser()
                return {"result": chain.invoke({"input": input_dict["input"]})}

            else:  # "general"
                return general_qa({"query": input_dict["input"]})

    return Router()